Undesirable noise has long been a problem in a variety of environments, including those associated with travel and working. Many of these environments generate repetitive noise or vibration that can become extremely annoying over time. One example of such an environment includes the engine sound from a plane or train during travel. In some cases, particularly those involving work environments, daily repeated exposure to undesirable noise may lead to work fatigue and other more serious medical conditions.
Active noise control (ANC) systems attempt to moderate the effects of undesirable noise by canceling at least a portion of such noise through the use of a secondary noise signal. The secondary noise signal thus interferes with and cancels much of the undesirable noise in the environment. So for many ANC systems, the undesirable noise is detected in the environment, and a secondary noise signal is generated of equal or similar amplitude and opposite phase. The secondary noise signal is then combined with the undesirable noise acoustically within the air of the environment, causing destructive interference with at least a portion of the undesirable noise. The combined acoustic wave in the environment is often monitored to determine any error signal between the undesirable noise and the secondary noise signal. Such an error signal represents the difference between the two noise signals, and thus indicates that a portion of the undesirable noise is not being canceled. The error signal can then be used to provide feedback to adjust the secondary noise signal to thus more effectively eliminate the undesirable noise.
In many cases, ANC systems have been somewhat successful for sound attenuation of frequencies below about 500 Hz. One of the earliest and simplest control algorithms developed was the least-mean-squares (LMS) algorithm. The LMS algorithm is based on a gradient descent approach that operates by adjusting the values of an adaptive finite impulse response (FIR) filter until the minimum mean squared error signal is obtained. The original LMS algorithm was not practical for acoustic applications because it did not account for the effects of the physical propagation of the control signal.
A related algorithm that accounts for the effects of the physical propagation, also known as the secondary path, is known as the filtered-x LMS (FXLMS) algorithm. This algorithm uses a reference signal input filtered with a FIR filter representing an estimate of the impulse response of the secondary path. In the frequency domain, this FIR filter would represent the transfer function of the secondary path. This secondary path estimate may include effects of digital-to-analog converters, reconstruction filters, audio power amplifiers, loudspeakers, the acoustic transmission path, error sensors, signal conditioning, anti-alias filters, analog-to-digital converters, etc. Although the FXLMS algorithm has been shown to be successful for some applications, it exhibits frequency dependant convergence and tracking behavior that may lead to significant degradation in the overall performance of the control system in some situations. The performance degradation is particularly evident for situations involving non-stationary noise where the target noise is likely to take on every frequency in the range where control is possible. One example of such non-stationary noise occurs in the cab of a tractor, where noise frequencies fluctuate with the tractor engine. In these cases, less attenuation is seen at the frequencies where the convergence of the algorithm is slow. Various other algorithms have been attempted, however most of these approaches either increase the computational burden of the algorithm, increase the complexity of the algorithm, or are only effective for specific applications. A second example where performance degradation occurs is noise characterized by multiple tones in the noise signal. One example of such noise occurs in the cabin of a helicopter, where tones corresponding to the engine speed, main rotor, and tail rotor exist simultaneously. In general, convergence of the algorithm is slow at one or more of these frequencies.